Sync from v0.13
This commit is contained in:
283
vllm/model_executor/layers/resampler.py
Normal file
283
vllm/model_executor/layers/resampler.py
Normal file
@@ -0,0 +1,283 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
||||
|
||||
# Adapted from
|
||||
# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
|
||||
# https://huggingface.co/Qwen/Qwen-7B/blob/main/modeling_qwen.py
|
||||
# https://github.com/facebookresearch/mae/blob/efb2a8062c206524e35e47d04501ed4f544c0ae8/util/pos_embed.py#L20
|
||||
#
|
||||
# Copyright 2023 The Qwen team.
|
||||
# Copyright 2023 The vLLM team.
|
||||
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
|
||||
# and OPT implementations in this library. It has been modified from its
|
||||
# original forms to accommodate minor architectural differences compared
|
||||
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""
|
||||
Shared resampler perceiver network used in multimodal models and
|
||||
related helpers for sincos positional embeddings.
|
||||
|
||||
Example models: Qwen (Qwen-VL), MiniCPM-V 2.0
|
||||
"""
|
||||
|
||||
import math
|
||||
from collections.abc import Callable
|
||||
from functools import partial
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
from torch import nn
|
||||
|
||||
from vllm.model_executor.layers.linear import ReplicatedLinear
|
||||
from vllm.model_executor.layers.quantization import QuantizationConfig
|
||||
|
||||
DEFAULT_LN = partial(nn.LayerNorm, eps=1e-6)
|
||||
|
||||
|
||||
def get_abs_pos(abs_pos: torch.Tensor, tgt_size: torch.Tensor | int) -> torch.Tensor:
|
||||
# abs_pos: L, C
|
||||
# tgt_size: (H, W)
|
||||
# return: M, C
|
||||
src_size = int(math.sqrt(abs_pos.size(0)))
|
||||
dtype = abs_pos.dtype
|
||||
if isinstance(tgt_size, int):
|
||||
tgt_size = (tgt_size, tgt_size)
|
||||
if src_size == tgt_size[0] and src_size == tgt_size[1]:
|
||||
return abs_pos
|
||||
return (
|
||||
F.interpolate(
|
||||
abs_pos.float().reshape(1, src_size, src_size, -1).permute(0, 3, 1, 2),
|
||||
size=(tgt_size[0], tgt_size[1]),
|
||||
mode="bicubic",
|
||||
align_corners=False,
|
||||
)
|
||||
.permute(0, 2, 3, 1)
|
||||
.flatten(0, 2)
|
||||
.to(dtype=dtype)
|
||||
)
|
||||
|
||||
|
||||
# sin/cos positional embedding helpers are adapted from:
|
||||
# https://github.com/facebookresearch/mae/blob/efb2a8062c206524e35e47d04501ed4f544c0ae8/util/pos_embed.py#L20
|
||||
def get_1d_sincos_pos_embed_from_grid(
|
||||
embed_dim: int, pos: np.ndarray, version: tuple[int, int] = (2, 0)
|
||||
) -> torch.Tensor:
|
||||
"""
|
||||
embed_dim: output dimension for each position
|
||||
pos: a list of positions to be encoded: size (M,) / (H, W)
|
||||
out: (M, D) / (H, W, D)
|
||||
"""
|
||||
assert embed_dim % 2 == 0
|
||||
omega = np.arange(embed_dim // 2, dtype=np.float32)
|
||||
omega /= embed_dim / 2.0
|
||||
omega = 1.0 / 10000**omega # (D/2,)
|
||||
|
||||
if version == (2, 0):
|
||||
pos = pos.reshape(-1) # (M,)
|
||||
out = np.einsum("m,d->md", pos, omega) # (M, D/2), outer product
|
||||
emb_sin = np.sin(out) # (M, D/2)
|
||||
emb_cos = np.cos(out) # (M, D/2)
|
||||
emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)
|
||||
else:
|
||||
out = np.einsum("hw,d->hwd", pos, omega) # (H, W, D/2), outer product
|
||||
emb_sin = np.sin(out) # (H, W, D/2)
|
||||
emb_cos = np.cos(out) # (H, W, D/2)
|
||||
emb = np.concatenate([emb_sin, emb_cos], axis=-1) # (H, W, D)
|
||||
return emb
|
||||
|
||||
|
||||
def get_2d_sincos_pos_embed_from_grid(
|
||||
embed_dim: int, grid: np.ndarray, version: tuple[int, int] = (2, 0)
|
||||
) -> torch.Tensor:
|
||||
assert embed_dim % 2 == 0
|
||||
|
||||
# use half of dimensions to encode grid_h
|
||||
emb_h = get_1d_sincos_pos_embed_from_grid(
|
||||
embed_dim // 2, grid[0], version
|
||||
) # (H*W, D/2) or (H, W, D/2)
|
||||
emb_w = get_1d_sincos_pos_embed_from_grid(
|
||||
embed_dim // 2, grid[1], version
|
||||
) # (H*W, D/2) or (H, W, D/2)
|
||||
|
||||
if version == (2, 0):
|
||||
emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
|
||||
else:
|
||||
emb = np.concatenate([emb_h, emb_w], axis=-1) # (H, W, D)
|
||||
return emb
|
||||
|
||||
|
||||
def get_2d_sincos_pos_embed(
|
||||
embed_dim: int,
|
||||
grid_size: int | tuple[int, int],
|
||||
cls_token: bool = False,
|
||||
version: tuple[int, int] = (2, 0),
|
||||
) -> torch.Tensor:
|
||||
"""
|
||||
grid_size: int of the grid height and width
|
||||
return:
|
||||
pos_embed: [grid_size*grid_size, embed_dim] or
|
||||
[1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
|
||||
"""
|
||||
if isinstance(grid_size, int):
|
||||
grid_h_size, grid_w_size = grid_size, grid_size
|
||||
else:
|
||||
grid_h_size, grid_w_size = grid_size[0], grid_size[1]
|
||||
|
||||
grid_h = np.arange(grid_h_size, dtype=np.float32)
|
||||
grid_w = np.arange(grid_w_size, dtype=np.float32)
|
||||
grid = np.meshgrid(grid_w, grid_h) # here w goes first
|
||||
grid = np.stack(grid, axis=0)
|
||||
assert isinstance(grid, np.ndarray) and grid.shape == (2, grid_h_size, grid_w_size)
|
||||
|
||||
if version == (2, 0):
|
||||
grid = grid.reshape([2, 1, grid_h_size, grid_w_size])
|
||||
pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid, version)
|
||||
if cls_token:
|
||||
pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0)
|
||||
else:
|
||||
pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid, version)
|
||||
return pos_embed
|
||||
|
||||
|
||||
class BaseResampler(nn.Module):
|
||||
"""
|
||||
A 2D perceiver-resampler network with one cross attention layers by
|
||||
(grid_size**2) learnable queries and 2d sincos pos_emb.
|
||||
Outputs:
|
||||
A tensor with the shape of (grid_size**2, embed_dim)
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
num_queries: int,
|
||||
embed_dim: int,
|
||||
num_heads: int,
|
||||
kv_dim: int | None = None,
|
||||
norm_layer: Callable[[int], nn.LayerNorm] = DEFAULT_LN,
|
||||
do_post_projection: bool = True,
|
||||
quant_config: QuantizationConfig | None = None,
|
||||
prefix: str = "",
|
||||
) -> None:
|
||||
super().__init__()
|
||||
|
||||
self.num_queries = num_queries
|
||||
self.embed_dim = embed_dim
|
||||
self.num_heads = num_heads
|
||||
|
||||
self.query = nn.Parameter(torch.empty(self.num_queries, embed_dim))
|
||||
|
||||
if kv_dim is not None and kv_dim != embed_dim:
|
||||
self.kv_proj = ReplicatedLinear(
|
||||
kv_dim,
|
||||
embed_dim,
|
||||
bias=False,
|
||||
quant_config=quant_config,
|
||||
prefix=f"{prefix}.kv_proj",
|
||||
)
|
||||
else:
|
||||
# Maintain the same return value with ReplicatedLinear.forward
|
||||
self.kv_proj = lambda *args, **kwargs: ( # type: ignore # noqa
|
||||
nn.Identity()(*args, **kwargs),
|
||||
None,
|
||||
)
|
||||
self.attn = nn.MultiheadAttention(embed_dim, num_heads)
|
||||
self.ln_q = norm_layer(embed_dim)
|
||||
self.ln_kv = norm_layer(embed_dim)
|
||||
self.do_post_projection = do_post_projection
|
||||
if self.do_post_projection:
|
||||
self.ln_post = norm_layer(embed_dim)
|
||||
data = (embed_dim**-0.5) * torch.empty(embed_dim, embed_dim)
|
||||
self.proj = nn.Parameter(data=data)
|
||||
|
||||
def _repeat(self, query, N: int):
|
||||
return query.unsqueeze(1).repeat(1, N, 1)
|
||||
|
||||
|
||||
class Resampler2(BaseResampler):
|
||||
"""Resampler-perceiver network to be used for a variety of model types,
|
||||
e.g., Qwen-vl / Minicpmv 2.0. The main difference is the addition of the
|
||||
do_post_projection arg, which indicates whether or not there should be
|
||||
a post layer normalization and projector after the attention. This is
|
||||
present in minicpmv2.0, but not qwen-vl.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
grid_size: int,
|
||||
embed_dim: int,
|
||||
num_heads: int,
|
||||
kv_dim: int | None = None,
|
||||
norm_layer: Callable[[int], nn.LayerNorm] = DEFAULT_LN,
|
||||
adaptive: bool = False,
|
||||
do_post_projection: bool = True,
|
||||
quant_config: QuantizationConfig | None = None,
|
||||
prefix: str = "",
|
||||
) -> None:
|
||||
super().__init__(
|
||||
grid_size**2,
|
||||
embed_dim,
|
||||
num_heads,
|
||||
kv_dim,
|
||||
norm_layer,
|
||||
do_post_projection=do_post_projection,
|
||||
quant_config=quant_config,
|
||||
prefix=prefix,
|
||||
)
|
||||
|
||||
self.adaptive = adaptive
|
||||
pos_embed_arr = get_2d_sincos_pos_embed(embed_dim, grid_size, version=(2, 0))
|
||||
|
||||
self.pos_embed = nn.Parameter(
|
||||
torch.from_numpy(pos_embed_arr).requires_grad_(False)
|
||||
)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x: torch.Tensor,
|
||||
tgt_sizes: torch.Tensor | None = None,
|
||||
attn_mask: torch.Tensor | None = None,
|
||||
) -> torch.Tensor:
|
||||
if tgt_sizes is None:
|
||||
tgt_sizes = int(math.sqrt(x.size(1)))
|
||||
if self.adaptive:
|
||||
pos_embed_arr = get_2d_sincos_pos_embed(
|
||||
self.embed_dim, tgt_sizes, version=(2, 0)
|
||||
)
|
||||
pos_embed = torch.from_numpy(pos_embed_arr).to(
|
||||
device=x.device, dtype=x.dtype
|
||||
)
|
||||
else:
|
||||
pos_embed = get_abs_pos(self.pos_embed, tgt_sizes).to(
|
||||
device=x.device, dtype=x.dtype
|
||||
)
|
||||
|
||||
x, _ = self.kv_proj(x)
|
||||
x = self.ln_kv(x).permute(1, 0, 2)
|
||||
|
||||
N = x.shape[1]
|
||||
q = self.ln_q(self.query)
|
||||
out = self.attn(
|
||||
self._repeat(q, N) + self.pos_embed.unsqueeze(1),
|
||||
x + pos_embed.unsqueeze(1),
|
||||
x,
|
||||
attn_mask=attn_mask,
|
||||
)[0]
|
||||
x = out.permute(1, 0, 2)
|
||||
if self.do_post_projection:
|
||||
x = self.ln_post(x)
|
||||
x = x @ self.proj
|
||||
return x
|
||||
Reference in New Issue
Block a user